Data Process for Indoor Positioning based on WiFi Fingerprint

Xuerong Cui; Mengyan Wang; Juan Li; Meiqi Ji; Jianhang Liu; Tingpei Huang; Haihua Chen

12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China

Research Article

Data Process for Indoor Positioning based on WiFi Fingerprint

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@INPROCEEDINGS{10.4108/eai.29-6-2019.2282793,
    author={Xuerong  Cui and Mengyan  Wang and Juan  Li and Meiqi  Ji and Jianhang  Liu and Tingpei  Huang and Haihua  Chen},
    title={Data Process for Indoor Positioning based on WiFi Fingerprint},
    proceedings={12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China},
    publisher={EAI},
    proceedings_a={MOBIMEDIA},
    year={2019},
    month={6},
    keywords={normal distribution kalman filter received signal strength indication fingerprint database},
    doi={10.4108/eai.29-6-2019.2282793}
}

Xuerong Cui
Mengyan Wang
Juan Li
Meiqi Ji
Jianhang Liu
Tingpei Huang
Haihua Chen
Year: 2019
Data Process for Indoor Positioning based on WiFi Fingerprint
MOBIMEDIA
EAI
DOI: 10.4108/eai.29-6-2019.2282793

Xuerong Cui^,*, Mengyan Wang¹, Juan Li¹, Meiqi Ji¹, Jianhang Liu¹, Tingpei Huang¹, Haihua Chen¹

1: Department of Computer and Communication Engineering, China University of Petroleum (East China)

*Contact email: cxr@upc.edu.cn

Abstract

Currently, most of the existing location fingerprint indoor positioning algorithms are based on the original fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. A method based on skewness-kurtosis normality test and Kalman filter fusion is proposed in this paper. Experiments shows that the fusion algorithm can effectively remove the abrupt data and noise fluctuations for the RSSI (Received Signal Strength Indication) data, and achieve accurate and smooth output of the RSSI value.

Keywords: normal distribution kalman filter received signal strength indication fingerprint database

Published: 2019-06-05
Publisher: EAI

: http://dx.doi.org/10.4108/eai.29-6-2019.2282793